{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6161aec",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e837e16",
   "metadata": {},
   "source": [
    "# Evaluate Realism and Diversity of the generated images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dcfe817",
   "metadata": {},
   "source": [
    "This notebook illustrates how to use MONAI to compute the most common metrics to evaluate the performance of a generative model. The metrics that we will analyse on this tutorial are:\n",
    "\n",
    "- Frechet Inception Distance (FID) [1] and Maximum Mean Discrepancy (MMD) [2], two metrics commonly used to assess the realism of generated image\n",
    "\n",
    "- the MS-SSIM [3] and SSIM [4] used to evaluate the image diversity\n",
    "\n",
    "\n",
    "[1] - Heusel et al., \"Gans trained by a two time-scale update rule converge to a local nash equilibrium\", https://arxiv.org/pdf/1706.08500.pdf\n",
    "\n",
    "[2] - Gretton et al., \"A Kernel Two-Sample Test\", https://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf\n",
    "\n",
    "[3] - Wang et al., \"Multiscale structural similarity for image quality assessment\", https://ieeexplore.ieee.org/document/1292216\n",
    "\n",
    "[4] - Wang et al., \"Image quality assessment: from error visibility to structural similarity\", https://ieeexplore.ieee.org/document/1284395\n",
    "\n",
    "[5] - Mei et al., \"RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning, https://pubs.rsna.org/doi/10.1148/ryai.210315"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80769612",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "551c5e61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/venn/Desktop/monai-code/monai_service_env/lib/python3.10/site-packages/ignite/handlers/checkpoint.py:17: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.\n",
      "  from torch.distributed.optim import ZeroRedundancyOptimizer\n"
     ]
    }
   ],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab694d15",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "629c60fc",
   "metadata": {
    "lines_to_end_of_cell_marker": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/venn/Desktop/monai-code/monai_service_env/lib/python3.10/site-packages/ignite/handlers/checkpoint.py:17: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.\n",
      "  from torch.distributed.optim import ZeroRedundancyOptimizer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.4.0rc1\n",
      "Numpy version: 1.26.0\n",
      "Pytorch version: 2.4.0+cu121\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: 34ce94db424445b38eb56a6c842e55a2122d4a9d\n",
      "MONAI __file__: /home/<username>/Desktop/monai-code/monai_service_env/lib/python3.10/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.11\n",
      "ITK version: 5.4.0\n",
      "Nibabel version: 5.2.1\n",
      "scikit-image version: 0.23.2\n",
      "scipy version: 1.14.0\n",
      "Pillow version: 10.4.0\n",
      "Tensorboard version: 2.17.0\n",
      "gdown version: 5.2.0\n",
      "TorchVision version: 0.19.0+cu121\n",
      "tqdm version: 4.66.5\n",
      "lmdb version: 1.5.1\n",
      "psutil version: 6.0.0\n",
      "pandas version: 2.2.2\n",
      "einops version: 0.8.0\n",
      "transformers version: 4.40.2\n",
      "mlflow version: 2.15.1\n",
      "pynrrd version: 1.0.0\n",
      "clearml version: 1.16.3\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tempfile\n",
    "import shutil\n",
    "from itertools import combinations\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torchvision.models as models\n",
    "\n",
    "from monai import transforms\n",
    "from monai.apps import MedNISTDataset\n",
    "from monai.config import print_config\n",
    "from monai.data import DataLoader, Dataset\n",
    "from monai.utils import set_determinism\n",
    "import urllib.request\n",
    "\n",
    "from monai.inferers import LatentDiffusionInferer\n",
    "from monai.metrics import FIDMetric, MMDMetric, MultiScaleSSIMMetric, SSIMMetric\n",
    "from monai.networks.nets import AutoencoderKL, DiffusionModelUNet\n",
    "from monai.networks.schedulers import DDPMScheduler\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52dbd59a",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the MONAI_DATA_DIRECTORY environment variable.\n",
    "This allows you to save results and reuse downloads.\n",
    "\n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0b189f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/tmpmdq5jz_7\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d79c501",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39c4b986",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e5a5d1",
   "metadata": {},
   "source": [
    "## Define the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2bdf536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "195db858",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoderkl = AutoencoderKL(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=1,\n",
    "    channels=(128, 128, 256),\n",
    "    latent_channels=3,\n",
    "    num_res_blocks=2,\n",
    "    attention_levels=(False, False, False),\n",
    "    with_encoder_nonlocal_attn=False,\n",
    "    with_decoder_nonlocal_attn=False,\n",
    ")\n",
    "autoencoderkl = autoencoderkl.to(device)\n",
    "autoencoderkl.eval()\n",
    "\n",
    "unet = DiffusionModelUNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=3,\n",
    "    out_channels=3,\n",
    "    num_res_blocks=2,\n",
    "    channels=(128, 256, 512),\n",
    "    attention_levels=(False, True, True),\n",
    "    num_head_channels=(0, 256, 512),\n",
    "    use_combined_linear=True,\n",
    ")\n",
    "unet = unet.to(device)\n",
    "unet.eval()\n",
    "\n",
    "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"linear_beta\", beta_start=0.0015, beta_end=0.0195)\n",
    "\n",
    "inferer = LatentDiffusionInferer(scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05d9e13",
   "metadata": {},
   "source": [
    "## Load pre-trained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0e81539b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNet weights loaded\n",
      "AutoencoderKL weights loaded\n"
     ]
    }
   ],
   "source": [
    "unet_url = (\n",
    "    \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/\"\n",
    "    \"tutorial_generation_2d_ldm_unet.pth\"\n",
    ")\n",
    "unet_path = unet_url.split(\"/\")[-1]\n",
    "urllib.request.urlretrieve(unet_url, unet_path)\n",
    "unet.load_state_dict(torch.load(unet_path, weights_only=True))\n",
    "print(\"UNet weights loaded\")\n",
    "autoencoderkl_url = (\n",
    "    \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/\"\n",
    "    \"tutorial_generation_2d_ldm_autoencoderkl.pth\"\n",
    ")\n",
    "autoencoderkl_path = autoencoderkl_url.split(\"/\")[-1]\n",
    "urllib.request.urlretrieve(autoencoderkl_url, autoencoderkl_path)\n",
    "autoencoderkl.load_state_dict(torch.load(autoencoderkl_path, weights_only=True))\n",
    "print(\"AutoencoderKL weights loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c187146",
   "metadata": {},
   "source": [
    "## Get the real images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b42415",
   "metadata": {},
   "source": [
    "Similar to the 2D LDM tutorial, we will use the MedNISTDataset, which contains images from different body parts. For easiness, here we will use only the `Hand` class. The first part of the code will get the real images from the MedNISTDataset and apply some transformations to scale the intensity of the image. Because we are evaluating the performance of the trained network, we will only use the validation split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd4c90f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MedNIST.tar.gz: 59.0MB [00:03, 18.1MB/s]                              "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-08-15 18:49:59,084 - INFO - Downloaded: /tmp/tmpmdq5jz_7/MedNIST.tar.gz\n",
      "2024-08-15 18:49:59,159 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "2024-08-15 18:49:59,159 - INFO - Writing into directory: /tmp/tmpmdq5jz_7.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading dataset: 100%|██████████| 5895/5895 [00:02<00:00, 2617.68it/s]\n"
     ]
    }
   ],
   "source": [
    "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n",
    "val_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.LoadImaged(keys=[\"image\"]),\n",
    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
    "        transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n",
    "    ]\n",
    ")\n",
    "val_ds = Dataset(data=val_datalist, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=180, shuffle=True, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e6facbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:12<00:00, 80.44it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some synthetic data for visualisation\n",
    "n_synthetic_images = 3\n",
    "noise = torch.randn((n_synthetic_images, 3, 16, 16))\n",
    "noise = noise.to(device)\n",
    "scheduler.set_timesteps(num_inference_steps=1000)\n",
    "\n",
    "with torch.no_grad():\n",
    "    syn_images = inferer.sample(\n",
    "        input_noise=noise, diffusion_model=unet, scheduler=scheduler, autoencoder_model=autoencoderkl\n",
    "    )\n",
    "\n",
    "# Plot 3 examples from the synthetic data\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3)\n",
    "for image_n in range(3):\n",
    "    ax[image_n].imshow(syn_images[image_n, 0, :, :].cpu(), cmap=\"gray\")\n",
    "    ax[image_n].axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5676aa62",
   "metadata": {},
   "source": [
    "## Compute FID"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98452f3f",
   "metadata": {},
   "source": [
    "The FID measures the distance between the feature vectors from the real images and those obtained from generated images.\n",
    "\n",
    "In order to compute the FID the images need to be passed into a pre-trained network to get the desired feature vectors.\n",
    "\n",
    "Although the FID is commonly computed using the Inception network, here, we used a pre-trained version of the ResNet-50 to calculate the feature space.\n",
    "\n",
    "Lower FID scores indicate that the images are more similar, with a perfect score being 0 indicating that the two groups of images are identical.\n",
    "\n",
    "Please note that the ResNet-50 used here is only for example. Extracting features from networks that are trained on [RadImagenet](https://github.com/BMEII-AI/RadImageNet) may get lower FID scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24130292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pre-trained ResNet-50 model\n",
    "resnet50 = models.resnet50(weights=\"ResNet50_Weights.DEFAULT\").to(device)\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "resnet50.eval()\n",
    "print(\"ResNet-50 model loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9faca46",
   "metadata": {},
   "source": [
    "Here, we will load the real and generate synthetic images from noise and compute the FID of these two groups of images.\n",
    "\n",
    "Because we are generating the synthetic images on this code snippet, the entire cell will take about 5 mins run and most of this time is spent in generating the images.\n",
    "\n",
    "The loading bars show how long it will take to complete the image generation for each mini-batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb0b579d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforms to convert the input image to the format expected by the model\n",
    "def subtract_mean(x: torch.Tensor) -> torch.Tensor:\n",
    "    \"\"\"Normalize an input image by subtracting the mean.\"\"\"\n",
    "    mean = [0.406, 0.456, 0.485]\n",
    "    x[:, 0, :, :] -= mean[0]\n",
    "    x[:, 1, :, :] -= mean[1]\n",
    "    x[:, 2, :, :] -= mean[2]\n",
    "    return x\n",
    "\n",
    "\n",
    "def get_features(image, model):\n",
    "    \"\"\"Get features from the input image.\"\"\"\n",
    "    # If input has just 1 channel, repeat channel to have 3 channels\n",
    "    if image.shape[1]:\n",
    "        image = image.repeat(1, 3, 1, 1)\n",
    "\n",
    "    # Change order from 'RGB' to 'BGR'\n",
    "    image = image[:, [2, 1, 0], ...]\n",
    "\n",
    "    # Subtract mean used during training\n",
    "    image = subtract_mean(image)\n",
    "\n",
    "    # Get model outputs\n",
    "    with torch.no_grad():\n",
    "        feature_image = model.forward(image)\n",
    "\n",
    "    return feature_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1b48d18c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:54<00:00, 18.27it/s]\n",
      "100%|██████████| 1000/1000 [00:55<00:00, 17.99it/s]\n",
      "100%|██████████| 1000/1000 [00:56<00:00, 17.82it/s]\n",
      "100%|██████████| 1000/1000 [00:56<00:00, 17.73it/s]\n",
      "100%|██████████| 1000/1000 [00:56<00:00, 17.62it/s]\n",
      "100%|██████████| 1000/1000 [00:35<00:00, 27.86it/s]\n"
     ]
    }
   ],
   "source": [
    "synth_features = []\n",
    "real_features = []\n",
    "\n",
    "for _step, x in enumerate(val_loader):\n",
    "    # Get the real images\n",
    "    real_images = x[\"image\"].to(device)\n",
    "\n",
    "    # Generate some synthetic images using the defined model\n",
    "    n_synthetic_images = len(x[\"image\"])\n",
    "    noise = torch.randn((n_synthetic_images, 3, 16, 16))\n",
    "    noise = noise.to(device)\n",
    "    scheduler.set_timesteps(num_inference_steps=1000)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        syn_images = inferer.sample(\n",
    "            input_noise=noise, diffusion_model=unet, scheduler=scheduler, autoencoder_model=autoencoderkl\n",
    "        )\n",
    "\n",
    "        # Get the features for the real data\n",
    "        real_eval_feats = get_features(real_images, resnet50)\n",
    "        real_features.append(real_eval_feats)\n",
    "\n",
    "        # Get the features for the synthetic data\n",
    "        synth_eval_feats = get_features(syn_images, resnet50)\n",
    "        synth_features.append(synth_eval_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1bcc49bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FID Score: 2954.0412\n"
     ]
    }
   ],
   "source": [
    "synth_features = torch.vstack(synth_features)\n",
    "real_features = torch.vstack(real_features)\n",
    "\n",
    "fid = FIDMetric()\n",
    "fid_res = fid(synth_features, real_features)\n",
    "\n",
    "print(f\"FID Score: {fid_res.item():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b50e92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 3 examples from the synthetic data\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3)\n",
    "for image_n in range(3):\n",
    "    ax[image_n].imshow(syn_images[image_n, 0, :, :].cpu(), cmap=\"gray\")\n",
    "    ax[image_n].axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba4e62d",
   "metadata": {},
   "source": [
    "# Compute MMD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa01253",
   "metadata": {},
   "source": [
    "Because the realism of the LDMs will depend on the realism of the autoencoder reconstructions, we will compute the MMD betweeen the original images and the reconstructed images to evaluate the performance of the autoencoder.\n",
    "\n",
    "MMD (Maximum Mean Discrepancy) is a distance metric used to measure the similarity between two probability distributions. This metric maps the samples from each distribution to a high-dimensional feature space and calculates the distance between the mean of the features of each distribution. A smaller MMD value indicates a better match between the real and generated distributions. It is often used in combination with other evaluation metrics to assess the performance of a generative model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0d92309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MS-SSIM score: -2.3441 +- 0.8068\n"
     ]
    }
   ],
   "source": [
    "mmd_scores = []\n",
    "\n",
    "mmd = MMDMetric()\n",
    "\n",
    "for _step, x in list(enumerate(val_loader)):\n",
    "    image = x[\"image\"].to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        image_recon = autoencoderkl.reconstruct(image)\n",
    "\n",
    "    mmd_scores.append(mmd(image, image_recon))\n",
    "\n",
    "mmd_scores = torch.stack(mmd_scores)\n",
    "print(f\"MS-SSIM score: {mmd_scores.mean().item():.4f} +- {mmd_scores.std().item():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98f914c",
   "metadata": {},
   "source": [
    "# Compute MultiScaleSSIMMetric and SSIMMetric\n",
    "\n",
    "SSIM measures the similarity between two images based on three components: luminance, contrast, and structure. In addition, MS-SSIM is an extension of SSIM that computes the structural similarity measure at multiple scales. Both metrics can assume values between 0 and 1, where 1 indicates perfect similarity between the images.\n",
    "\n",
    "There are two ways to compute the MS-SSIM and SSIM, and in this notebook we will look at both ways:\n",
    "1. Use the reconstructions of the autoencoder and the real images. By using the metric this way we can assess the performance of the autoencoder.\n",
    "2. Compute the MS-SSIM and SSIM between pairs of synthetic images. This second way of computing the MS-SSIM can be used as a metric to evaluate the diversity of the synthetic images.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd139cb5",
   "metadata": {},
   "source": [
    "In this section we will compute the MS-SSIM and SSIM Meteric between the real images and those reconstructed by the AutoencoderKL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb2cd8a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MS-SSIM Metric: 0.9906425 +- 0.0036851\n",
      "SSIM Metric: 0.9138125 +- 0.0212628\n"
     ]
    }
   ],
   "source": [
    "ms_ssim_recon_scores = []\n",
    "ssim_recon_scores = []\n",
    "\n",
    "ms_ssim = MultiScaleSSIMMetric(spatial_dims=2, data_range=1.0, kernel_size=4)\n",
    "ssim = SSIMMetric(spatial_dims=2, data_range=1.0, win_size=4)\n",
    "\n",
    "for _step, x in list(enumerate(val_loader)):\n",
    "    image = x[\"image\"].to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        image_recon = autoencoderkl.reconstruct(image)\n",
    "\n",
    "    ms_ssim_recon_scores.append(ms_ssim(image, image_recon))\n",
    "    ssim_recon_scores.append(ssim(image, image_recon))\n",
    "\n",
    "ms_ssim_recon_scores = torch.cat(ms_ssim_recon_scores, dim=0)\n",
    "ssim_recon_scores = torch.cat(ssim_recon_scores, dim=0)\n",
    "\n",
    "print(f\"MS-SSIM Metric: {ms_ssim_recon_scores.mean():.7f} +- {ms_ssim_recon_scores.std():.7f}\")\n",
    "print(f\"SSIM Metric: {ssim_recon_scores.mean():.7f} +- {ssim_recon_scores.std():.7f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ad94fd",
   "metadata": {},
   "source": [
    "Compute the SSIM and MS-SSIM between pairs of synthetic images, the results of the MS-SSIM and SSIM can be used to evaluate the diversity of the synthetic samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7e189159",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [00:01<00:00, 17.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MS-SSIM Metric: 0.0338 +- 0.0260\n",
      "SSIM Metric: 0.0834 +- 0.0069\n"
     ]
    }
   ],
   "source": [
    "ms_ssim_scores = []\n",
    "ssim_scores = []\n",
    "\n",
    "# How many synthetic images we want to generate\n",
    "n_synthetic_images = 10\n",
    "\n",
    "# Generate some synthetic images using the defined model\n",
    "noise = torch.randn((n_synthetic_images, 3, 64, 64))\n",
    "noise = noise.to(device)\n",
    "scheduler.set_timesteps(num_inference_steps=25)\n",
    "\n",
    "with torch.no_grad():\n",
    "    syn_images = inferer.sample(\n",
    "        input_noise=noise, diffusion_model=unet, scheduler=scheduler, autoencoder_model=autoencoderkl\n",
    "    )\n",
    "\n",
    "    idx_pairs = list(combinations(range(n_synthetic_images), 2))\n",
    "    for idx_a, idx_b in idx_pairs:\n",
    "        ms_ssim_scores.append(ms_ssim(syn_images[[idx_a]], syn_images[[idx_b]]))\n",
    "        ssim_scores.append(ssim(syn_images[[idx_a]], syn_images[[idx_b]]))\n",
    "\n",
    "\n",
    "ms_ssim_scores = torch.cat(ms_ssim_scores, dim=0)\n",
    "ssim_scores = torch.cat(ssim_scores, dim=0)\n",
    "\n",
    "print(f\"MS-SSIM Metric: {ms_ssim_scores.mean():.4f} +- {ms_ssim_scores.std():.4f}\")\n",
    "print(f\"SSIM Metric: {ssim_scores.mean():.4f} +- {ssim_scores.std():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd99f0d",
   "metadata": {},
   "source": [
    "# Clean-up data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2bd7167",
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
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